#! /usr/bin/env python
#
def p00_data ( prob, dim_num, data_num ):

#*****************************************************************************80
#
## P00_DATA returns the data for any problem.
#
#  Licensing:
#
#    This code is distributed under the GNU LGPL license.
#
#  Modified:
#
#    29 June 2015
#
#  Author:
#
#    John Burkardt
#
#  Parameters:
#
#    Input, integer PROB, the problem index.
#
#    Input, integer DIM_NUM, the spatial dimension of the dependent
#    variables.
#
#    Input, integer DATA_NUM, the number of data points.
#
#    Output, real P_DATA(DIM_NUM,DATA_NUM), the data.
#
  from sys import exit

  if ( prob == 1 ):
    p_data = p01_data ( dim_num, data_num )
  elif ( prob == 2 ):
    p_data = p02_data ( dim_num, data_num )
  elif ( prob == 3 ):
    p_data = p03_data ( dim_num, data_num )
  elif ( prob == 4 ):
    p_data = p04_data ( dim_num, data_num )
  elif ( prob == 5 ):
    p_data = p05_data ( dim_num, data_num )
  elif ( prob == 6 ):
    p_data = p06_data ( dim_num, data_num )
  elif ( prob == 7 ):
    p_data = p07_data ( dim_num, data_num )
  elif ( prob == 8 ):
    p_data = p08_data ( dim_num, data_num )
  else:
    print ( '' )
    print ( 'P00_DATA - Fatal error!' )
    print ( '  Unexpected input value of PROB.' )
    exit ( 'P00_DATA - Fatal error!' )

  return p_data

def p01_data ( dim_num, data_num ):

#*****************************************************************************80
#
## P01_DATA returns the data for problem p01.
#
#  Licensing:
#
#    This code is distributed under the GNU LGPL license.
#
#  Modified:
#
#    29 June 2015
#
#  Author:
#
#    John Burkardt
#
#  Parameters:
#
#    Input, integer DIM_NUM, the spatial dimension of the dependent
#    variables.
#
#    Input, integer DATA_NUM, the number of data points.
#
#    Output, real P_DATA(DIM_NUM,DATA_NUM), the data.
#
  import numpy as np

  p_data = np.array ( [ \
    [  0.0, 4.0 ], \
    [  1.0, 5.0 ], \
    [  2.0, 6.0 ], \
    [  4.0, 6.0 ], \
    [  5.0, 5.0 ], \
    [  6.0, 3.0 ], \
    [  7.0, 1.0 ], \
    [  8.0, 1.0 ], \
    [  9.0, 1.0 ], \
    [ 10.0, 3.0 ], \
    [ 11.0, 4.0 ], \
    [ 12.0, 4.0 ], \
    [ 13.0, 3.0 ], \
    [ 14.0, 3.0 ], \
    [ 15.0, 4.0 ], \
    [ 16.0, 4.0 ], \
    [ 17.0, 3.0 ], \
    [ 18.0, 0.0 ] ] )

  p_data = np.transpose ( p_data )

  return p_data

def p02_data ( dim_num, data_num ):

#*****************************************************************************80
#
## P02_DATA returns the data for problem p02.
#
#  Discussion:
#
#    Two pairs of identical X values have now been slightly separated.
#
#  Licensing:
#
#    This code is distributed under the GNU LGPL license.
#
#  Modified:
#
#    29 June 2015
#
#  Author:
#
#    John Burkardt
#
#  Parameters:
#
#    Input, integer DIM_NUM, the spatial dimension of the dependent
#    variables.
#
#    Input, integer DATA_NUM, the number of data points.
#
#    Output, real P_DATA(DIM_NUM,DATA_NUM), the data.
#
  import numpy as np

  p_data = np.array ( [ \
   [  0.00,   0.00 ], \
   [  1.34,   5.00 ], \
   [  5.00,   8.66 ], \
   [ 10.00,  10.00 ], \
   [ 10.60,  10.40 ], \
   [ 10.70,  12.00 ], \
   [ 10.705, 28.60 ], \
   [ 10.80,  30.20 ], \
   [ 11.40,  30.60 ], \
   [ 19.60,  30.60 ], \
   [ 20.20,  30.20 ], \
   [ 20.295, 28.60 ], \
   [ 20.30,  12.00 ], \
   [ 20.40,  10.40 ], \
   [ 21.00,  10.00 ], \
   [ 26.00,   8.66 ], \
   [ 29.66,   5.00 ], \
   [ 31.00,   0.00 ] ] )

  p_data = np.transpose ( p_data )

  return p_data

def p03_data ( dim_num, data_num ):

#*****************************************************************************80
#
## P03_DATA returns the data for problem p03.
#
#  Licensing:
#
#    This code is distributed under the GNU LGPL license.
#
#  Modified:
#
#    29 June 2015
#
#  Author:
#
#    John Burkardt
#
#  Parameters:
#
#    Input, integer DIM_NUM, the spatial dimension of the dependent
#    variables.
#
#    Input, integer DATA_NUM, the number of data points.
#
#    Output, real P_DATA(DIM_NUM,DATA_NUM), the data.
#
  import numpy as np

  p_data = np.array ( [ \
    [  0.0,  0.0 ], \
    [  2.0, 10.0 ], \
    [  3.0, 10.0 ], \
    [  5.0, 10.0 ], \
    [  6.0, 10.0 ], \
    [  8.0, 10.0 ], \
    [  9.0, 10.5 ], \
    [ 11.0, 15.0 ], \
    [ 12.0, 50.0 ], \
    [ 14.0, 60.0 ], \
    [ 15.0, 85.0 ] ] )

  p_data = np.transpose ( p_data )

  return p_data

def p04_data ( dim_num, data_num ):

#*****************************************************************************80
#
## P04_DATA returns the data for problem p04.
#
#  Licensing:
#
#    This code is distributed under the GNU LGPL license.
#
#  Modified:
#
#    29 June 2015
#
#  Author:
#
#    John Burkardt
#
#  Parameters:
#
#    Input, integer DIM_NUM, the spatial dimension of the dependent
#    variables.
#
#    Input, integer DATA_NUM, the number of data points.
#
#    Output, real P_DATA(DIM_NUM,DATA_NUM), the data.
#
  import numpy as np

  p_data = np.array ( [ \
   [ 0.00,  0.00 ], \
   [ 0.05,  0.70 ], \
   [ 0.10,  1.00 ], \
   [ 0.20,  1.00 ], \
   [ 0.80,  0.30 ], \
   [ 0.85,  0.05 ], \
   [ 0.90,  0.10 ], \
   [ 1.00,  1.00 ] ] )

  p_data = np.transpose ( p_data )

  return p_data

def p05_data ( dim_num, data_num ):

#*****************************************************************************80
#
## P05_DATA returns the data for problem p05.
#
#  Licensing:
#
#    This code is distributed under the GNU LGPL license.
#
#  Modified:
#
#    29 June 2015
#
#  Author:
#
#    John Burkardt
#
#  Parameters:
#
#    Input, integer DIM_NUM, the spatial dimension of the dependent
#    variables.
#
#    Input, integer DATA_NUM, the number of data points.
#
#    Output, real P_DATA(DIM_NUM,DATA_NUM), the data.
#
  import numpy as np

  p_data = np.array ( [ \
    [ 0.00, 0.00 ], \
    [ 0.10, 0.90 ], \
    [ 0.20, 0.95 ], \
    [ 0.30, 0.90 ], \
    [ 0.40, 0.10 ], \
    [ 0.50, 0.05 ], \
    [ 0.60, 0.05 ], \
    [ 0.80, 0.20 ], \
    [ 1.00, 1.00 ] ] )

  p_data = np.transpose ( p_data )

  return p_data

def p06_data ( dim_num, data_num ):

#*****************************************************************************80
#
## P06_DATA returns the data for problem p06.
#
#  Licensing:
#
#    This code is distributed under the GNU LGPL license.
#
#  Modified:
#
#    29 June 2015
#
#  Author:
#
#    John Burkardt
#
#  Parameters:
#
#    Input, integer DIM_NUM, the spatial dimension of the dependent
#    variables.
#
#    Input, integer DATA_NUM, the number of data points.
#
#    Output, real P_DATA(DIM_NUM,DATA_NUM), the data.
#
  import numpy as np

  p_data = np.array ( [ \
   [ 595.0, 0.644 ], \
   [ 605.0, 0.622 ], \
   [ 615.0, 0.638 ], \
   [ 625.0, 0.649 ], \
   [ 635.0, 0.652 ], \
   [ 645.0, 0.639 ], \
   [ 655.0, 0.646 ], \
   [ 665.0, 0.657 ], \
   [ 675.0, 0.652 ], \
   [ 685.0, 0.655 ], \
   [ 695.0, 0.644 ], \
   [ 705.0, 0.663 ], \
   [ 715.0, 0.663 ], \
   [ 725.0, 0.668 ], \
   [ 735.0, 0.676 ], \
   [ 745.0, 0.676 ], \
   [ 755.0, 0.686 ], \
   [ 765.0, 0.679 ], \
   [ 775.0, 0.678 ], \
   [ 785.0, 0.683 ], \
   [ 795.0, 0.694 ], \
   [ 805.0, 0.699 ], \
   [ 815.0, 0.710 ], \
   [ 825.0, 0.730 ], \
   [ 835.0, 0.763 ], \
   [ 845.0, 0.812 ], \
   [ 855.0, 0.907 ], \
   [ 865.0, 1.044 ], \
   [ 875.0, 1.336 ], \
   [ 885.0, 1.881 ], \
   [ 895.0, 2.169 ], \
   [ 905.0, 2.075 ], \
   [ 915.0, 1.598 ], \
   [ 925.0, 1.211 ], \
   [ 935.0, 0.916 ], \
   [ 945.0, 0.746 ], \
   [ 955.0, 0.672 ], \
   [ 965.0, 0.627 ], \
   [ 975.0, 0.615 ], \
   [ 985.0, 0.607 ], \
   [ 995.0, 0.606 ], \
   [1005.0, 0.609 ], \
   [1015.0, 0.603 ], \
   [1025.0, 0.601 ], \
   [1035.0, 0.603 ], \
   [1045.0, 0.601 ], \
   [1055.0, 0.611 ], \
   [1065.0, 0.601 ], \
   [1075.0, 0.608 ] ] )

  p_data = np.transpose ( p_data )

  return p_data

def p07_data ( dim_num, data_num ):

#*****************************************************************************80
#
## P07_DATA returns the data for problem p07.
#
#  Licensing:
#
#    This code is distributed under the GNU LGPL license.
#
#  Modified:
#
#    29 June 2015
#
#  Author:
#
#    John Burkardt
#
#  Parameters:
#
#    Input, integer DIM_NUM, the spatial dimension of the dependent
#    variables.
#
#    Input, integer DATA_NUM, the number of data points.
#
#    Output, real P_DATA(DIM_NUM,DATA_NUM), the data.
#
  import numpy as np

  p_data = np.array ( [ \
   [ 0.0, 1.0 ], \
   [ 1.0, 2.0 ], \
   [ 4.0, 2.0 ], \
   [ 5.0, 1.0 ] ] )

  p_data = np.transpose ( p_data )

  return p_data

def p08_data ( dim_num, data_num ):

#*****************************************************************************80
#
## P08_DATA returns the data for problem p08.
#
#  Licensing:
#
#    This code is distributed under the GNU LGPL license.
#
#  Modified:
#
#    29 June 2015
#
#  Author:
#
#    John Burkardt
#
#  Parameters:
#
#    Input, integer DIM_NUM, the spatial dimension of the dependent
#    variables.
#
#    Input, integer DATA_NUM, the number of data points.
#
#    Output, real P_DATA(DIM_NUM,DATA_NUM), the data.
#
  import numpy as np

  p_data = np.array ( [ \
   [ -1.0, 1.00 ], \
   [ -0.8, 0.64 ], \
   [ -0.6, 0.36 ], \
   [ -0.4, 0.16 ], \
   [ -0.2, 0.04 ], \
   [  0.0, 0.00 ], \
   [  0.2, 0.04 ], \
   [  0.20001, 0.05 ], \
   [  0.4, 0.16 ], \
   [  0.6, 0.36 ], \
   [  0.8, 0.64 ], \
   [  1.0, 1.00 ] ] )

  p_data = np.transpose ( p_data )

  return p_data

def p00_data_test ( ):

#*****************************************************************************80
#
## P00_DATA_TEST tests P00_DATA.
#
#  Licensing:
#
#    This code is distributed under the GNU LGPL license.
#
#  Modified:
#
#    29 June 2015
#
#  Author:
#
#    John Burkardt
#
  import platform
  from p00_data import p00_data
  from p00_data_num import p00_data_num
  from p00_dim_num import p00_dim_num
  from p00_prob_num import p00_prob_num
  from r8mat_transpose_print import r8mat_transpose_print

  print ( '' )
  print ( 'P00_DATA_TEST tests P00_DATA' )
  print ( '  Python version: %s' % ( platform.python_version ( ) ) )
  print ( '  P00_DATA returns the actual (MxN) data for any problem.' )

  prob_num = p00_prob_num ( )

  for prob in range ( 1, prob_num + 1 ):

    print ( '' )
    print ( '  Problem %d' % ( prob ) )

    data_num = p00_data_num ( prob )
    print ( '  DATA_NUM = %d' % ( data_num ) )

    dim_num = p00_dim_num ( prob )
    print ( '  DIM_NUM  = %d' % ( dim_num ) )

    p = p00_data ( prob, dim_num, data_num )

    r8mat_transpose_print ( dim_num, data_num, p, '  Data array:' )
#
#  Terminate.
#
  print ( '' )
  print ( 'P00_DATA_TEST:' )
  print ( '  Normal end of execution.' )
  return

if ( __name__ == '__main__' ):
  from timestamp import timestamp
  timestamp ( )
  p00_data_test ( )
  timestamp ( )